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• W3034878846 endingPage "108071" @default.
• W3034878846 startingPage "108071" @default.
• W3034878846 abstract "Recently, domain adaptation (DA) algorithms have been extensively employed in many fault diagnosis applications. Most prior researches perform well under a general assumption: the distributions of samples are balanced. Nevertheless, the unbalanced distributions of samples are common in practical applications which may cause the performances of these researches drop dramatically. To overcome this deficiency, an enhanced transfer joint matching (TJM) approach is proposed in this paper. Two main contributions are concluded as follows. (1) To our knowledge, it is a pioneering work to apply the maximum variance discrepancy (MVD) for combining with the maximum mean discrepancy (MMD) for the feature matching. (2) The row ℓ2-norm is applied for different domains to improve the generalization ability of the proposed model. In addition, z-score normalization is adopted for the softmax regression classifier. Comprehensive experimental results validate that the enhanced TJM can significantly outperform competitive approached for cross-domain bearing defect diagnosis." @default.
• W3034878846 created "2020-06-19" @default.
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• W3034878846 date "2020-12-01" @default.
• W3034878846 modified "2023-09-30" @default.
• W3034878846 title "Unsupervised domain adaptation via enhanced transfer joint matching for bearing fault diagnosis" @default.
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• W3034878846 doi "https://doi.org/10.1016/j.measurement.2020.108071" @default.
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